Abstract: Intestinal dysbiosis is common in people living with HIV/AIDS (PLWH), yet fungal communities of the gut microbiota (mycobiota) remain poorly characterized, especially in severely immunosuppressed patients. We analyzed the gut mycobiota of 33 PLWH and 20 healthy controls from a public hospital in central Argentina. Most PLWH presented severe immunosuppression (<200 CD4+ T cells/μL) and acute or chronic diarrhea, with or without antibiotic exposure or antiretroviral therapy. Fecal DNA was extracted and the ITS2 region was sequenced using next-generation sequencing. Beta-diversity analyses revealed significant segregation between PLWH and controls (PERMANOVA, Adonis: p = 0.001, R² = 0.0989). LEfSe analysis identified 17 fungal species enriched in PLWH, predominantly Candida albicans, Candida dubliniensis, and Nakaseomyces glabratus, whereas 31 species were more abundant in controls, including Penicillium spp., Candida sake, and Clavispora lusitaniae. Histoplasma capsulatum, an endemic pathogen in the region, was more prevalent in PLWH and associated with CD4+T-cell counts. Dirichlet multinomial mixture analysis revealed two mycobiotypes: M1, with a balanced fungal composition predominating in controls, and M2, dominated by Candida species and present in PLWH. These findings provide novel insights into gut mycobiota alterations in severely immunosuppressed PLWH in Argentina, highlighting Candida-driven dysbiosis and the regional relevance of H. capsulatum.

Abstract: Urban scaling theory establishes that socioeconomic outputs scale superlinearly with city population (β > 1), attributed to social-interaction density, but its applicability to resource-constrained sectors remains untested. We analyse a panel of ∼ 2 , 800 Chinese counties (2000–2023) with GDP decomposed into primary, secondary, and tertiary sectors. Using the urbanization ratio as a continuous moderator in interaction-term regressions, we estimate sector-specific crossover thresholds from sub- to super-linear scaling; a Scale-Adjusted Agricultural Index (SAAI) quantifies each county’s deviation from size-expected output. A robust sectoral spectrum emerges—βpri = 0.87 < βter = 0.96 < βsec = 1.08—whose rank order is preserved across all 24 sample years. The tertiary sector crosses β = 1 at urbanization ratio u∗ = 0.80 (95% CI [0.72, 0.92]), with interaction coefficient β1 = 1.48 (p < 0.001). Province fixed effects confirm the urbanization interaction for secondary and tertiary sectors (p < 0.001) but not primary (p = 0.248), consistent with the crossover being specific to interaction-intensive activities. China’s 832 designated poverty counties exhibit systematically negative SAAI values (Cohen’s d = 0.55–0.87), revealing a persistent scaling deficit that conventional output comparisons obscure. These results show that the scaling exponent is a continuous function of economic structure, identify a quantifiable urbanization threshold for the onset of increasing returns, and supply a boundary condition for Bettencourt’s theory of urban scaling.

Abstract: Genomic regulation is typically interpreted through observable molecular states such as gene expression, chromatin accessibility and epigenetic modifications. However, biological systems also contain large reservoirs of genomic information that remain transcriptionally inactive for extended periods while retaining the capacity to influence future regulatory behaviour. This phenomenon, referred to here as gene latency, suggests that genomes may preserve forms of biological memory beyond currently expressed molecular states. Recent advances in artificial intelligence—particularly transformer-based architectures—demonstrate how complex systems can encode structured information within latent representational spaces that influence outputs without continuous activation (Vaswani et al., 2017; Brown et al., 2020). In this study, we propose a conceptual framework that interprets gene latency as a form of genomic memory using principles derived from latent representation learning in artificial intelligence. By aligning concepts from systems biology, epigenetics and machine learning, we outline theoretical and computational perspectives for identifying latent regulatory potential within genomic systems. This framework suggests that genomes may contain distributed reservoirs of regulatory capacity shaped by developmental history and environmental exposure. Integrating artificial intelligence with genomic theory may therefore enable new approaches for modelling latent regulatory states and predicting transitions from genomic latency to activation.

Abstract: Hemoglobinopathies are common inherited blood disorders, affecting 7% of the global population. In India, β-thalassemia, sickle cell disease, and other variants show varied prevalence across regions and ethnic groups due to genetic diversity and consanguinity. This study analyzed the demographic profile and prevalence of hemoglobinopathies among 4,336 patients registered at the Thalassemia and Sickle Cell Society (TSCS), Hyderabad. Of the 4,336 cases registered from 1998–2025, the highest were in 2023 (9.85%), with minimal contributions from 1998–2002 (each < 1.2%). Blood samples were evaluated using CBC and HPLC to assess RBC indices and hemoglobin fractions. Among 4,336 individuals, 57.7% were male and 42.2% female, with mean age of 16.24 years. Most patients were aged 10–20 years (39.58%) and about 39.4% reported consanguineous marriages. Most patients were from Telangana (71.2%) and Andhra Pradesh (23.8%). Hinduism was the predominant religion (76.4%), with Lambadi, Madiga, and Mala being the most represented castes. Beta-thalassemia major was the most prevalent disorder (59.96%), followed by sickle cell disease (22.3%) and sickle beta-thalassemia (9.1%). Other less common hemoglobinopathies included E beta-thalassemia, thalassemia intermedia, delta beta thalassemia and rare variants HbH disease and HPFH. These findings underscore the significant public health burden of hemoglobinopathies in Telangana. The high prevalence of β-thalassemia, highlights the urgent need for targeted genetic screening, counseling, and community-based awareness programs. A coordinated approach involving early detection, multidisciplinary care, and advanced therapies is essential to reduce disease impact. Institutions like TSCS exemplify a successful model of integrated care, combining diagnostics, treatment, and patient support.

Abstract: In standard quantum mechanics, the electron is treated as a fundamental particle whose wavefunction describes a spatial probability distribution. While this formalism provides extremely accurate predictions, the conceptual relationship between orbital geometry, particle localization, and wave–particle duality remains interpretatively open. In this work, we propose a geometric reinterpretation within the framework of Viscous Time Theory (VTT). In this view, atomic orbitals arise as stabilized basins of informational curvature within a viscous informational manifold, and the electron emerges as the undissipated residual of this geometric formation. By introducing the Informational Hessian as the curvature tensor associated with coherence deviation ΔC, orbital stability can be formulated as a positive-definite curvature condition over the informational manifold. Within this framework, electron mass is reinterpreted as an integrated curvature excess associated with stabilized orbital geometry. This approach provides: (i) a geometric interpretation of wave–particle duality as periodic coherence recall, (ii) a reinterpretation of excited states as metastable curvature attractors, and (iii) a potential structural mechanism for residual mass generation within stabilized informational structures. The proposed framework is presented as a constructive extension compatible with Schrödinger dynamics. Rather than replacing the standard formalism, we suggest the existence of a deeper geometric layer whose implications invite further mathematical and physical investigation.

Abstract: Antimicrobial resistance is often framed as a problem acquired outside the patient through transmission of resistant strains and genes. This view is important, but it is incomplete for immunocompromised patients, where there is substantial evidence that drug-resistant bacteria can evolve within the host during therapy. In haematological malignancy, transplantation, and other states of impaired immunity, infections persist longer, immune clearance is reduced, and prolonged use of last-line antibiotics creates repeated selection events. These conditions favour stepwise evolution toward the hardest-to-treat phenotypes, including carbapenem resistance, tigecycline resistance, colistin resistance, and resistance to ceftazidime–avibactam, often alongside persistence in reservoirs such as the gastrointestinal tract. This essay argues that antibiotic escalation alone is therefore an incomplete strategy in these settings and that care should be explicitly evolution-aware. Adjunct and alternative approaches should be prioritised earlier to reduce bacterial burden, shorten time under selection, and limit reliance on prolonged sequential antibiotic regimens. Bacteriophages are highlighted as one promising adjunct because they are highly specific, generally well tolerated, can self-amplify at sites where susceptible bacteria are present, and can be iterated through approaches such as training and rational cocktails. Phage–antibiotic synergy is also discussed as a practical strategy to improve killing and reduce escape.

Abstract: Intracranial fusiform aneurysms represent a rare but clinically aggressive subtype of cerebrovascular disease, characterized by circumferential arterial dilation and a high risk of growth, ischemic complications, and rupture. Unlike saccular aneurysms, fusiform lesions lack well-established medical therapies to prevent progression or stabilize the aneurysm wall. Tumor necrosis factor–alpha (TNF-α) has emerged as a central mediator of aneurysm-associated inflammation and vascular remodeling, raising interest in TNF-α modulation as a potential therapeutic strategy. To systematically review and synthesize the available clinical and translational evidence evaluating TNF-α signaling and anti–TNF-α therapies in the context of intracranial fusiform aneurysms. A systematic literature search was conducted in PubMed/MEDLINE, Embase, and Google Scholar from database inception through February 2026 in accordance with PRISMA guidelines. Eligible studies included human, animal, and translational investigations examining TNF-α biology or anti–TNF-α interventions in relation to intracranial fusiform aneurysms, intracranial dolichoectasia, or vertebrobasilar dolichoectatic aneurysms. Study selection, deduplication, and screening were performed using Covidence systematic review software. Extracted outcomes included aneurysm growth, rupture, ischemic events, imaging characteristics, inflammatory signaling, and vascular remodeling. Given substantial heterogeneity in study design and outcome reporting, findings were synthesized narratively using structured evidence mapping. From 368 records identified, 14 studies met inclusion criteria following full-text review. Included studies encompassed preclinical models, translational mechanistic investigations, and limited clinical observational data. Across experimental models, TNF-α signaling was consistently associated with macrophage infiltration, matrix metalloproteinase activation, vascular smooth muscle cell phenotypic modulation, and aneurysm wall degeneration. TNF-α inhibition was associated with reduced aneurysm progression and rupture in preclinical settings, including when initiated after aneurysm formation. Clinical evidence remains limited but suggests a potential association between TNF-α modulation and aneurysm stability, although direct therapeutic data in intracranial fusiform aneurysm populations are sparse. Existing translational and preclinical evidence supports a contributory role for TNF-α–mediated inflammation in the progression of intracranial fusiform aneurysms and suggests that TNF-α inhibition may represent a promising disease-modifying strategy. However, clinical data remain insufficient to support routine therapeutic use. Prospective observational studies and early-phase clinical trials are needed to define the safety, timing, and efficacy of anti–TNF-α therapies in patients with intracranial fusiform aneurysms.

Abstract: In side-channel analysis, simple power analysis (SPA) is a widely used technique for recovering secret information by exploiting differences between operations in traces. However, in realistic measurement environments, SPA is often hindered by noise, temporal misalignment, and weak or transient leakage, which obscure secret-dependent features in single or very few power traces. In this paper, we provide a systematic analysis of moving-skewness-based trace preprocessing for enhancing asymmetric leakage characteristics relevant to SPA. The method computes local skewness within a moving window along the trace, transforming the original signal into a skewness trace that emphasizes distributional asymmetry while suppressing noise. Unlike conventional smoothing-based preprocessing techniques, the proposed approach preserves and can even amplify subtle leakage patterns and spike-like transient events that are often attenuated by low-pass filtering or moving-average methods. To further improve applicability under different leakage conditions, we introduce feature-driven window-selection strategies that align preprocessing parameters with various leakage characteristics. Both simulated datasets and real measurement traces collected from multiple cryptographic platforms are used to evaluate the effectiveness of the approach. Experimental results indicate that moving-skewness preprocessing improves leakage visibility and achieves higher SPA success rates compared to commonly used preprocessing methods.

Abstract: Polycystic ovary syndrome (PCOS) is a prevalent endocrine–metabolic disorder affecting 5.5–11.5% of women of reproductive age. While reduced adiponectin levels have been con-sistently demonstrated in adult women with PCOS, findings in adolescents remain less clearly defined. A systematic review and meta-analysis was conducted in accordance with PRISMA guidelines. PubMed, Embase, Scopus, and Google Scholar were searched from inception to October 31, 2025. Observational studies comparing adiponectin levels in post-pubertal adolescents with PCOS and controls were included. A random-effects model with REML estimator was applied. Study heterogeneity and publication bias were as-sessed. Eighteen studies comprising 1,590 participants (679 PCOS; 911 controls) were in-cluded. Adolescents with PCOS demonstrated significantly lower adiponectin levels com-pared to controls (mean difference [MD] −3.17 µg/mL; 95% CI −4.27 to −2.07; p = 0.001), I² = 94.6%. Egger’s (p = 0.81) and Begg’s (p = 0.16) tests indicated no evidence of publication bias. Adolescents with PCOS exhibit significantly reduced circulating adiponectin levels, suggesting that adipose tissue dysfunction and metabolic dysregulation are present early in the disease course. These findings support the role of adiponectin as a potential early biomarker of cardiometabolic risk in adolescent PCOS and underscore the importance of early metabolic screening and intervention.

Abstract: Objective: The study investigates the effects of modeling liquids (MLs) on the staining of composite resins, with a focus on unichromatic resins. Materials and methods: The research was carried out by subjecting samples of monochromatic resin (2mm height x 6mm internal diameter) to the immersion protocol in coffee solution (Nescafé Tradição Forte), with color evaluation after 21 days. Results: Statistics showed that the adhesive group presented greater color change when compared to the modeling group (p = .000). There was no statistically significant difference between water and experimental staining (= 0.104). Among the staining group factors, there was a difference for ∆E in the interactions mC-mE (p = 0.004), mC-aC (p < 0.001), mC-aE (p < 0.001), cE-aC (p = 0.015), cE-aE (p = 0.007), cC-aC (p = 0.033) and cC-aE (p = 0.017). Conclusion: These results indicate the need for further clinical studies on the applicability of modeling liquids to support decision-making in clinical practice.

Abstract: Both heat and photon energy are integral parts of scientific research. The study of the photon and the electron does not present up-to-date science in some phenomena. A misconception falls at the basic level. To eliminate the misconception, a discussion presents the electron dynamics in the silicon atom. The electron executes confined interstate dynamics for one forward or reverse cycle. As a result, the resulting shaped force-energy defines a unit photon. That unit photon has a shape similar to a Gaussian distribution with turned ends. A featured photon can interact with the side of a laterally orientated electron (of a semisolid or solid atom) to possibly convert into heat energy. When a featured photon interacts with the tip of a laterally oriented electron, that photon can convert into energy bits. The shapes of energy bits are similar to integral symbols. The reference point for the electron executing confined interstate dynamics is the center of a silicon atom. The north-south tips of the electrons align along the north-south poles. The energy shapes around the force tracing along the trajectory of electron dynamics. To execute confined interstate dynamics, forces of the two poles appear conservatively for turning the electron each time. The outer ring electron of the silicon atom reaches the ‘maximum limit point’ during the confined interstate dynamics. There is energy of one bit. In the remaining half cycle, that electron also generates energy of one bit. The electron dynamics of the silicon atom generate photons of a wave shape. Atoms of some other elements generate photons other than wave shapes. The execution of the electron dynamics is nearly at the speed of light. In addition to energy science, the study is useful in physical and chemical sciences.

Abstract: The Colorado River and its tributaries housed in the Colorado River Basin (CRB) are the primary source of water to the western United States and the Republic of Mexico. The river system is under intense stress due to prolonged drought and anthropogenic activities which have worsened its water quality. Total dissolved solids (TDS) and total suspended solids (TSS) are two water quality parameters (WQPs) that are crucial to the sustainability of the river system. These parameters are noted to have caused varied severity to the sustenance of the basin’s water. Monitoring of these WQPs has been conventionally conducted using field and laboratory analysis which are cost and labor-intensive. This study utilized a novel method to effectively develop machine learning (ML) models to estimate TDS and TSS concentrations in the CRB by utilizing the potential of optically sensitive multispectral Sentinel 2 A/B Multispectral Scanners (MSI) and Landsat 8 Operational Land Imager (OLI) remote sensing (RS) data retrieved from the Google Earth Engine (GEE) and in situ measurements collected from 2013-2022. Several standalone models such as linear regressions (LR), ridge regressions (Ridge), lasso regressions (Lasso), and k-nearest neighbor (KNN), and ensemble methods including the gradient boosting machines (GBM), random forest (RF), adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), and bagging were applied for the accurate estimation of TDS and TSS. Results found ensemble models like the XGBoost as the most optimal model estimating TDS using images from both Sentinel-2 MSI and Landsat 8 OLI with performance on the external validation dataset derived as 0.99, 26.52 mg/L, and 19.19 mg/L, respectively for R², RMSE, and MAE for Sentinel-2 images. The XGBoost yielded R², RMSE, and MAE of 0.97, 35.82 mg/L, and 27.90 mg/L, respectively. The AdaBoost was found to be best model for TSS estimations with values of 0.92, 29.48 mg/L, and 24.64 mg/L, respectively, for R², RMSE, and MAE for the Sentinel-2 image on the external validation dataset. The RF model was found to be the optimal model for TSS estimations with the Landsat 8 OLI with reported R², RMSE, and MAE of 0.90, 32.80 mg/L, and 22.91 mg/L, respectively on the external validation dataset. These findings show great potential of utilizing RS data to produce cost-efficient spatiotemporal changes on the WQPs which has an important implication for the continuous management of the limited water resources. Further study should consider the effect of land use land cover, runoff, and other climatic data to understand the complexity and dynamics of these parameters on TDS and TSS in the river.

Abstract: Bhimbor Deori (1903-1947) remains a pivotal yet insufficiently explored figure in the history of India's struggle for independence and the political evolution of Assam. A multifaceted individual-lawyer, tribal rights advocate, parliamentarian, and nationalist leader, Deori played a crucial role in mobilising the plain tribal communities of Assam and was instrumental in countering colonial and Muslim League efforts to incorporate the province into the proposed state of Pakistan. This review synthesises the available biographical, historical, and political information to construct a comprehensive profile of the Deori. It critically examines his early life and the discriminatory incident that catalysed his public career, his foundational role in institutionalising tribal politics through the Assam Backwards Plains Tribal League, his tenure as a Legislative Councillor and Minister, and his strategic collaboration with Gopinath Bordoloi. This article analyses a significant duality in his legacy: his simultaneous advocacy for Indigenous self-determination and his unwavering commitment to a unified Indian nation. It also interrogates the ideological tensions between his advocacy for tribal "homelands" and his Indian nationalism. Finally, this article identifies significant gaps in the existing scholarship, which relies heavily on commemorative sources, and proposes concrete avenues for future archival and critical research to fully integrate Jananeta Bhimbor Deori into the broader historiography of modern South Asia.

Abstract: This study aims to prove the Riemann Hypothesis and the Generalized Riemann Hypothesis by ex-tending the Riemann zeta function and Dirichlet L -functions to the elliptic complex domain, based ona newly constructed system of elliptic complex numbers Cλ(λ < 0) . The core challenge addressed is theinherent difficulty in resolving these conjectures within the traditional ”circular complex domain” frame-work (λ = −1); the author posits that a complete proof is unattainable strictly within this conventionalsetting.The primary innovation of this work lies in the formulation of the theory of elliptic complex numbers,specifically identifying the limiting case as λ → 0− as the key to the proof. Through rigorous deduction,a bijective correspondence between zeros across different complex planes is established. By employingproof by contradiction and leveraging the correspondence between Cλ (as λ → 0) and the circle complexplane C, the Riemann Hypothesis and the Generalized Riemann Hypothesis are ultimately proven. Thispaper is organized into three parts:(1) Construction and Geometric Properties: The first part details the construction of elliptic complexnumbers and their fundamental geometric properties, laying the necessary foundation for subsequentanalysis and the proof of the conjectures.(2) Analytic Extension: The second part introduces elliptic complex numbers into mathematical anal-ysis, deriving numerous results analogous to those in classical complex variable function theory.(3) Proof of Conjectures: The final part presents the formal proofs of the Riemann Hypothesis and theGeneralized Riemann Hypothesis.

Abstract: This ethnobotanical study documents medicinal plant diversity and traditional healing practices in Barguna District, a coastal region of Bangladesh. Twenty-seven traditional healers (kabiraj) were interviewed using semi-structured questionnaires during April-June 2025. A total of 68 medicinal plant species representing 34 botanical families were documented. Fabaceae emerged as the most represented family (10.3%), followed by Lamiaceae (8.8%). Trees constituted the dominant growth form (35.3%), with leaves being the most frequently utilized plant part (32.4%). The documented species treat twelve major ailment categories, with gastrointestinal disorders (22.8%) being most prevalent. Informant Consensus Factor (FIC) values ranged from 0.62 to 0.89, with gastrointestinal disorders showing highest consensus (FIC = 0.89), followed by respiratory ailments (FIC = 0.85) and diabetes (FIC = 0.82). Citation Frequency (Cf) analysis revealed Azadirachta indica (Cf = 0.89), Ocimum sanctum (Cf = 0.81), and Curcuma longa (Cf = 0.78) as culturally most significant species. Decoction (34.6%) and paste application (23.4%) were predominant preparation methods, with oral administration (61.2%) being most common. The demographic profile indicated that 81.5% of healers acquired knowledge through family inheritance, highlighting intergenerational transmission patterns. However, this traditional knowledge faces erosion threats from modernization, with 44.4% of practitioners lacking formal education and 18.5% aged above 60 years. The study reveals substantial ethnomedicinal diversity in coastal ecosystems, emphasizing the urgent need for conservation strategies, sustainable harvesting protocols, and systematic pharmacological validation to preserve indigenous knowledge while supporting rural healthcare and drug discovery initiatives.

Abstract: Quantum Mechanics is sufficiently capable of proving quantum gravity by itself without considering actual Einsteinian General Relativistic formalism. Due to the non-applicability of Einsteinian relativity in quantum gravity, in this article, we have described gravity as a correspondence between General (Quantum) Relativity and Quantum Field Theory (QFT) by introducing a (quantum) quadratic form and a (quantum) metric tensor along with dynamic time t. Here, we have developed a Kline-Gordon-like equation and a Dirac-like equation in QFT, which are themselves actually nothing but the quantum gravitational field equations (analogous to Einstein's field equation in General Relativity) for bosons and fermions, respectively. Furthermore, we have developed a Generalized Quantum Gravitational Field Theory, where QFT is conjugated with gravity and Dark Energy (for inconstant cosmological constant), so that it can unify Standard Model with gravity and Dark Energy in 'General Unified Theory' as SU(5)=SU(3)×(SU(2)⊕iSU(2)) through a Gravito-weak symmetry group. In addition, we have shown that unbounded operators, such as, i) the (quantum) relativistic mass and time, ii) the quantum scalar curvature and the proper time, iii) the (quantum) relativistic mass and its inversely stretched/shrank (3+1)D curvilinear quantum spacetime, all in pairs are satisfied their individual Uncertainty Principles, i.e., they cannot have definite and constant values at the same time. We have also proved that the present theory of Quantum Gravity is 'multiplicatively renormalizable'.

Abstract: Physics education traditionally presents the discipline as a collection of established recipes, omitting the personal, a priori reflection central to the historical development of theories. The rise of Generative AI has exposed the fragility of this recipe-based model, as AI can now execute algorithmic problem-solving effortlessly. This theoretical paper argues that this technological disruption is a catalyst for a fundamental reorientation: the physics classroom must be reconceived as a space for structured thinking, where theories are presented as subjective, creative resolutions to problems, a direct reflection of an inventor's mind. We synthesize historical and philosophical analyses with contemporary physics education research (PER) on student epistemologies, conceptual change, and metacognition to propose a pedagogical framework centered on three pillars: acknowledging subjectivity in theory-building, recognizing multiple equivalent formulations, and leveraging historical narrative. We further introduce AI-assisted classroom strategies designed to implement this shift, positioning AI as a Socratic partner and historical simulator. The paper concludes with a set of testable propositions to guide future empirical research. This work contributes a novel theoretical synthesis that integrates AI into a historically-grounded, reflective pedagogy, aiming to cultivate the \textit{inventor's mind} and prepare students not merely as consumers of knowledge but as creators of future possibilities.

Abstract: Hybrid use of Terraform for infrastructure andAnsible for configuration is common on Azure, but the two toolsare often joined only by ad hoc scripts and fragile handoffs in CIpipelines. Runbook Mesh proposes a small MCP based controlplane that treats Terraform and Ansible as one coordinatedchange unit rather than two independent stages. Azure DevOpstriggers an MCP server that drives a deployment state machine:it receives Terraform plans and apply results, derives a dynamicAnsible inventory from Terraform outputs, and orchestratesconfiguration playbooks with drain, cordon, and health checksfor VM scale sets, AKS nodes, and virtual machines. TheMCP enforces simple invariants on ordering, handoff safety,and rollback reachability, and packages each deployment intoa witness bundle containing plan digests, state and inventoryhashes, play outcomes, and Azure Resource Graph snapshots.The result is an Azure native pattern where infrastructure andconfiguration share a single timeline, a defined rollback path, anda tamper evident change ledger suited to regulated environments.

Abstract: Background: Hematopoietic stem cell transplantation (HSCT) is a widely utilized subtype of transplantation employed in various malignant and non-malignant diseases, particularly when conventional treatments or therapeutics prove ineffective. Despite the frequent occurrence of post-transplantation lymphoproliferative disease (PTLD) in patients undergoing HSCT, no comprehensive global prevalence rate has been established to date. Methodology: In this study, we selected 39 studies from 941 studies from three databases (i.e., PubMed, ScienceDirect, and Google Scholar) to identify the global prevalence rate of PTLD in HSCT patients. Results: The pooled prevalence was determined as 5.6% (95% CI: 5.0 to 6.3) and increased to 12.4% (95% CI: 10.2 to 14.7) after excluding outlier studies. The quality of the studies was also high. The prevalence of death cases among HSCT patients was determined as 0.6% (95% CI: 0.4 to 0.9). PTLD was most prevalent in allogenic HSCT (i.e., 5.6% (95% CI: 4.9 to 6.3)) and within the European region (i.e., 27.1% (95% CI: 21.4 to 32.8)). Among risk factors, HLA mismatch was reported in most of the studies. Conclusion: This study assessed and discussed the overall global prevalence of PTLD in HSCT patients, continent-based prevalence, and risk factors that can be helpful in finding the possible prevention mechanism of PTLD and implementing individualized treatment approaches based on the treatment availability during HSCT.

Abstract: This study develops a leakage-safe PCA–APT framework that constructs an idiosyncratic market-stress index from cross-sectional residual dispersion and evaluates its usefulness for anticipating equity drawdowns. Using daily adjusted prices for SPY and 11 U.S. sector ETFs from 2020–2025, we compute sector excess returns (sector minus SPY), estimate a low-dimensional common component via principal component analysis (PCA), and define residual stress as the cross-sectional root-mean-square magnitude of PCA reconstruction residuals. To prevent look-ahead bias, the PCA mapping is estimated using information available only through t−1, stress is computed out-of-sample at t, and stress regimes are identified using a rolling train-only quantile threshold that is shifted forward by one trading day. Drawdown-warning performance is assessed using drawdown-onset events and early-warning classification metrics (ROC-AUC, PR-AUC, and horizon-H precision/recall). Empirically, residual stress spikes cluster around drawdown onsets and provides predictive information, although a volatility-based benchmark remains stronger on average across discrimination metrics. Importantly, residual stress exhibits state-dependent complementarity with volatility: conditional on low volatility, high residual stress is associated with a materially higher probability of a drawdown onset within the next H=21 trading days (approximately 17% vs. 8%), and the joint high-stress/high-volatility regime identifies the highest-risk states (approximately 36% onset probability). Event-level overlap diagnostics further indicate that residual stress can flag a subset of drawdown onsets not captured by a volatility-threshold rule, while some onsets are not preceded by either signal. Economic relevance is examined under transaction costs through (i) a residual-ranked sector long–short portfolio and (ii) stress-managed SPY overlays that reduce exposure during detected regimes. In the baseline sample, a volatility-managed overlay improves drawdown control relative to buy-and-hold, whereas the residual-stress overlay does not reduce maximum drawdown and the residual-ranked long–short strategy is not robustly profitable after costs. Overall, the paper contributes a reproducible, leakage-safe evaluation pipeline linking cross-sectional residual dispersion to drawdown risk and clarifies when residual stress serves as a complementary market-structure risk indicator alongside standard volatility-based signals.